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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.11513 |
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| _version_ | 1866910304510672896 |
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| author | Matchev, Konstantin T. Matcheva, Katia Ramond, Pierre Verner, Sarunas |
| author_facet | Matchev, Konstantin T. Matcheva, Katia Ramond, Pierre Verner, Sarunas |
| contents | Theoretical physicists describe nature by i) building a theory model and ii) determining the model parameters. The latter step involves the dual aspect of both fitting to the existing experimental data and satisfying abstract criteria like beauty, naturalness, etc. We use the Yukawa quark sector as a toy example to demonstrate how both of those tasks can be accomplished with machine learning techniques. We propose loss functions whose minimization results in true models that are also beautiful as measured by three different criteria - uniformity, sparsity, or symmetry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11513 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring the Truth and Beauty of Theory Landscapes with Machine Learning Matchev, Konstantin T. Matcheva, Katia Ramond, Pierre Verner, Sarunas High Energy Physics - Phenomenology Machine Learning Theoretical physicists describe nature by i) building a theory model and ii) determining the model parameters. The latter step involves the dual aspect of both fitting to the existing experimental data and satisfying abstract criteria like beauty, naturalness, etc. We use the Yukawa quark sector as a toy example to demonstrate how both of those tasks can be accomplished with machine learning techniques. We propose loss functions whose minimization results in true models that are also beautiful as measured by three different criteria - uniformity, sparsity, or symmetry. |
| title | Exploring the Truth and Beauty of Theory Landscapes with Machine Learning |
| topic | High Energy Physics - Phenomenology Machine Learning |
| url | https://arxiv.org/abs/2401.11513 |